With bus global positioning system data and smart-card data, this study puts forward improved algorithms to further increase the success rate of alighting stop identification for the student group, so that trip chains and origin-destination (OD) matrixes of students can be obtained with the high estimated rate. Aimed at the student group, after using and generalising conventional alighting algorithms, this study innovatively utilises resident and non-resident students’ typical trip patterns to further increase the success estimated rate. As a result, the algorithms are verified with a correct estimation of 74.9%, where the success rate steeply increases by 8.6% through the method based on students’ typical trip patterns. The empirical analysis and application have shown that the methodology can observe the trip chains of students and optimise the bus service for students. In the future, OD matrixes obtained should be validated with bus OD surveys, and traffic system of metro and shared bikes as well as other datasets such as land use data will be taken into consideration.
Improved algorithms for trip-chain estimation using massive student behaviour data from urban transit systems
IET Intelligent Transport Systems ; 13 , 3 ; 435-442
2018-10-10
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
urban transit systems , student group , smart-card data , bus service optimisation , road vehicles , shared bikes , Global Positioning System , bus OD surveys , smart cards , land use data , matrix algebra , data analysis , trip-chain estimation , nonresident students typical trip pattern , alighting stop identification , bus global positioning system data , massive student behaviour data , traffic system , resident students typical trip pattern , road traffic , OD matrixes , metro bikes
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